1. Field of the Invention
The present invention relates generally to natural language spoken dialog systems and, more particularly, to an active learning process for spoken dialog systems.
2. Introduction
Voice-based natural dialog systems enable customers to express what they want in spoken natural language. Such systems automatically extract the meaning from speech input and act upon what people actually say, in contrast to what one would like them to say, shifting the burden from the users to the machine. In a natural language spoken dialog system, identifying the speaker's intent can be seen as a general classification problem. Once the speaker's intent is determined, the natural language spoken dialog system can take actions accordingly to satisfy their request.
In a natural language spoken dialog system, the speaker's utterance is recognized first using an automatic speech recognizer component. Then, the intent of the speaker is identified (or classified) from the recognized speech sequence using a spoken language understanding component.
When statistical recognizers and classifiers are employed, they are trained using large amounts of task data, which are transcribed and annotated by humans. This transcription and labeling process is a very expensive and laborious process. What is needed therefore is a process that enables the building of better statistical recognition and classification systems in a shorter time frame.